1. Field of the Invention
Generally, the present invention relates to the field of fabricating integrated circuits, and, more particularly, to monitoring and measuring process tool characteristics of a process tool used for the fabrication of semiconductor devices or other microstructures.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in the field of microstructure fabrication, for instance for manufacturing semiconductor devices, since, in this field, it is essential to combine cutting-edge technology with mass production techniques. It is, therefore, the goal of manufacturers of semiconductors, or generally of microstructures, to reduce the consumption of raw materials and consumables while at the same time improving process tool utilization. The latter aspect is especially important, since, in modern semiconductor facilities, equipment is required which is extremely cost intensive and represents the dominant part of the total production costs. At the same time, the process tools of a semiconductor facility have to be replaced more frequently compared to most other technical fields due to the rapid development of new products and processes, which may also demand correspondingly adapted process tools.
Integrated circuits are typically manufactured in automated or semi-automated facilities, thereby passing through a large number of process and metrology steps to complete the device. The number and the type of process steps and metrology steps a semiconductor device has to go through depends on the specifics of the semiconductor device to be fabricated. For instance, a sophisticated CPU requires several hundred process steps, each of which has to be carried out within specified process margins so as to fulfill the specifications for the device under consideration.
Consequently, a plurality of process tools operating on the basis of predefined process recipes substantially determine the throughput and yield of a semiconductor facility, wherein the individual reliability, availability and maintainability of the process tools has a significant influence on the overall yield and product quality. For this reason, it is of great importance for the semiconductor manufacturer to monitor and determine corresponding metrics that provide a measure for the performance of individual process tools, thereby also enabling tool suppliers to specifically improve software and hardware components of process tools on the basis of the data provided by the manufacturers. Since tool requirements may significantly depend on manufacturer-specific conditions, a plurality of industrial standards have been defined to provide a foundation for defining a common global set of semiconductor equipment requirements, thereby reducing company-specific requirements for production equipment while, on the supplier side, attention may be focused on improving process capabilities instead of maintaining many customer-specific products. Thus, a plurality of equipment-specific standards have been defined relating to the definition and measurement of equipment reliability, availability and maintainability (RAM) known under SEMI (Semiconductor Equipment and Materials Institute) E10, which establish a common language for measuring RAM performance in a typical environment encountered in a facility for fabricating microstructures, such as integrated circuits. The E10 standard, which is widely adopted by the industry to measure RAM performance of, for instance, process tools used in the semiconductor industry presently defines six basic tool states of a process state so as to categorize the tool condition at each time within a typical manufacturing environment:                (1) productive state (PRD)—specifying a regular operation of the process tool under consideration, that is, production runs and the like representing a period during which the process tool is performing its intended function;        (2) standby state (SBY)—that is, the process tool is available but not producing, i.e., this state represents a period in which the tool is not operated although it is in a condition to perform its intended function and the chemicals and facilities are available;        (3) engineering state (ENG)—that is, the process tool is available but engineering experiments are being run, such as process characterization, equipment evaluation and the like, thus the process tool is in a condition to perform its intended function and no tool or process problems exist;        (4) scheduled downtown state (SDT)—that is, a period during which the process tool is not available to perform its intended function due to planned downtime events, such as maintenance delay, production tests, preventative maintenance (PM), changing consumables, setup of process change, facilities related downtime and the like;        (5) unscheduled down time state—that is a period during which the process tool is not in a condition to perform its intended function due to unplanned downtime events, such as maintenance delay, repair, unforeseen change of consumables, out-of-specification inputs, unforeseen facilities-related downtime and the like; and        (6) unscheduled state—that is, a period of non-scheduled time during which the process tool is not scheduled to be utilized in production, such as periods including off-line training, unworked shifts, weekends, holidays and the like.        
Thus, based on these tool states, the total time of the process tool's “evolution” may be categorized, for instance in a non-scheduled time corresponding to the non-scheduled state and operations time corresponding to the states 1-5 as defined above. The operations time may then be divided into uptime and downtime, wherein the uptime may be further divided into engineering time and manufacturing time, wherein the latter includes a productive time and a standby time. Consequently, productive time, standby time and engineering time correspond to the states 1-3 defined above. On the other side, the downtime of the process tool may be divided into scheduled downtime and unscheduled downtime corresponding to the tool states 4 and 5 defined above.
Furthermore, appropriate metrics may be defined for the reliability, availability and maintainability (RAM) of a process tool in order to more completely monitor and measure the equipment behavior, which may then assist in providing information to the supplier and also enhancing productivity and process control. In this respect, tool reliability may be defined as the probability that the process tool under consideration will perform its intended function within stated conditions for a specified period of time. The tool availability may be defined as the number of hours in which the tool is producing plus the standby time divided by the total available hours, wherein the availability is typically expressed as a percentage. For example, 168−(facilities downtime+equipment downtime+engineering time+setup and test time)/168 hours×100.
Maintainability may be defined as the probability that the process tool will be retained in or restored to a condition in which it can perform its intended function within a specified period. For example, appropriate metrics for describing the reliability, availability and maintainability may include metrics such as mean time between interrupts (MTBI), mean time between failures (MTBF), mean time between assists (MTBA), mean time to repair (MTTR), uptime, downtime and utilization.
Consequently, great efforts are made during the operation of a semiconductor facility in quantitatively determining the behavior of the process tools, wherein automated data gathering techniques are typically used due to the high number of process tools producing a correspondingly high amount of process information. Recently, process tools have become more complex in that a process tool may include a plurality of functional modules or entities, referred to as cluster or cluster tool, which may operate in a parallel and/or sequential manner such that a product arriving at the cluster tool may be operated therein in a plurality of process paths, depending on the process recipe and the current tool state. The recipe may be understood as the computer program, rules, specifications, operations and procedures performed each time to produce a substrate that contains functional units. Consequently, a cluster tool recipe may be understood as a set of instructions for the processing of substrates through a sequence of integrated process modules or entities, wherein a process module may be understood as a functional unit of a process tool which may perform a specific operation and may communicate its individual process state to the environment, for instance to a manufacturing execution system (MES). Thus, the above-specified tool states may also correspond to each individual entity or process module, thereby considering each entity as an individual process tool.
Consequently, for equipment performance reporting, the entities forming a cluster tool may be tracked and monitored with respect to the independent E10 states defined above, while an assessment of the cluster tool as a whole is not provided. Therefore, it has been proposed to evaluate the state of a cluster tool as a series of systems in order to provide the ability for measuring the conventional E10 RAM metrics. In this approach, so-called intended process paths are defined and considered as separate entities, wherein the overall performance of the multi-path cluster tool is derived from the performance of the individual process paths. As previously stated, the states defined within the E10 standard may not allow handling multi-path cluster tools at an overall level but may be applied to the individual tool entities. Consequently, reliability, for instance in the form of mean time between failure (MTBF), availability, for instance in the form of operational uptime, and maintainability, for instance in the form of mean time to repair (MTTR), for the various tool entities may be calculated, wherein these metrics, however, do not provide a metric for the multi-path cluster tool as an entity itself.
With reference to FIGS. 1a-1b, the conventional technique for characterizing cluster tools on the basis of the E10 standard will be described in more detail. FIG. 1a schematically shows a cluster tool 150 comprising a plurality of entities 151 and 152, wherein the entities or modules 151 may represent transportation modules, such as load locks 151A, 151B for receiving substrates, while the entity 151C may represent an unload lock for outputting substrates processed by the process entities or modules 152, wherein, for instance, entities 152A and 152B may represent equivalent process chambers configured to perform substantially the same process, such as an etch process and the like, while a process entity 152C may be configured to perform a subsequent process, such as resist stripping, cleaning and the like. Consequently, a substrate arriving at the cluster tool 150 may be passed through the tool 150 according to a plurality of process paths, depending on tool-specific conditions, such as availability of one of the entities 151, 152, and the like. Each of the entities 151, 152 may be assessed on the basis of the states as defined above, wherein an assessment of the tool 150 as a whole may lead to less meaningful metrics, for instance when one of the process modules 152A, 152B is not capable of processing substrates for a specified time period, since in principle the cluster tool 150 would be considered as being productive at all times due to its capability of producing products on the basis of the remaining functional entity 152. Simultaneously, although being in a productive state, a failure may exist and may require equipment maintenance, thereby rendering the present definitions of uptime and downtime less effective for the cluster tool 150. As previously discussed, the cluster tool 150 may be divided into an aggregate of “virtual tools” by defining respective intended process paths for the cluster tool 150, wherein an automated state change data collection on entity level is typically required to effectively calculate RAM metrics for a multi-path cluster tool, such as the tool 150, especially if a plurality of tools having a more or less complex structure are used in a manufacturing environment. For the generic tool 150, two intended process paths may be defined such that the substrate arriving at the tool 150 may be handled by one of the load locks 151A, 151B and may be supplied to the entity 152A and subsequently to the entity 152C and may finally be output by the unload lock 151C. Similarly, a second process path may be defined by one of the load locks 151A, 151B, the module 152B, the module 152C and the unload lock 151C. The corresponding intended process paths may be identified as IPP1 and IPP2 and an operation uptime for the cluster tool 150 may be defined as follows: Operational uptime (multi-path cluster tool)=(Σuptime for all intended process paths)/((number of process paths)×(operations time, as defined above)))×100.
In order to determine the operation uptime, the availability of the respective intended process paths may be determined, which may be accomplished on the basis of a truth table, such as Table 1a.
TABLE 1a151A151B152A152B152C151CIPP 1IPP 2UpUpUpUpUpUpUpUpDownUpUpUpUpUpUpUpDownDownUpUpUpUpDownDownUpUpDownUpUpUpDownUpUpUpUpDownUpUpUpDownUpUpUpUpDownUpDownDownUpUpUpUpUpDownDownDown
For reducing the complexity of the cluster tool 150 for assessing the RAM metrics, the availability of the transport system may be considered separately in a respective truth table:
TABLE 1b151A151B151CTransport 151UpUpUpUpDownUpUpUpUpDownUpUpDownDownUpDownUpUpDownDown
Thus, as is evident from Table 1b, the transport system 151 is up when at least the unload lock 151C is up and at least one of the load locks 151A, 151B is up.
FIG. 1b schematically illustrates the cluster tool 150 when virtually separated into two process path entities IPP1 and IPP2, wherein the plurality of transportation modules or entities 151A, 151B, 151C are combined into an entity “transport” 151. Thus, on the basis of the tool 150 as illustrated in FIG. 1b, the availability of the tool 150 may be established on the basis of a truth table, which presents a combination of Tables 1b and 1b. Therefore, in Table 1c, the uptimes and downtimes of the respective entities IPP1 and IPP2, comprising the cluster tool 150 as configured in FIG. 1b, may be determined.
TABLE 1c152A152B152C151 TransportIPP 1IPP 2UpUpUpUpUpUpUpUpUpUpUpUpUpUpUpDownDownDownDownUpUpUpDownUpDownDownUpUpDownDownUpDownUpUpUpDownUpUpDownUpDownDownUpUpUpDownDownDown
As is evident from Table 1c, three tool configurations may result in a corresponding uptime of the entity IPP1 and respective three tool configurations, which may differ from the former configurations, result in a corresponding operational uptime of the entity IPP2. Consequently, on the basis of Table 1c and by measuring the respective states of the entities 152 and the transport 151 with respect to their temporal progression, respective operational uptimes and downtimes for a specified time period may be calculated. Moreover, other availability metrics according to the E10 standard may be calculated from the correspondingly established Table 1c. For instance, for an operations time of 168 hours, the evaluation of respective measurement results of the individual entity states may result in an uptime of entity IPP1 of 100 hours while the uptime of the entity IPP2 may be 140 hours. From these exemplary numbers, the operational uptime of the tool 150 may be calculated according to the above-specified formula, thereby resulting in an operational uptime of 71.4%. Other metrics with respect to reliability, availability and maintainability may be calculated on the basis of the above-specified procedures. For example, the mean time before failure (MTBF) for the cluster tool 150 may be calculated as the sum of the productive time for all process entities, that is, the entities 152 divided by the sum of failures during the productive time for all entities including the transport system 151. For the above-identified uptimes of IPP1 and IPP2, the following operational behavior of the cluster tool 150 may be assumed:
Entity 151A may have 100 productive hours with one failure, thereby resulting in an MTBF of 100 hours.
Entity 151B may have 140 productive hours and one failure may be assumed, thereby resulting in an MTBF of 140 hours.
Entity 151C may have 140 productive hours, since IPP2 has 140 hours uptime as specified above, and two failures are assumed, thereby resulting in an MTBF of 70 hours.
The transport system 151 may have one failure, thereby resulting in an MTBF of 140 hours.
Based on the above-given formula, the MTBF of the total cluster tool 150 may yield 380 hours/5 failures=76 hours.
Consequently, RAM metrics for the cluster tool 150 may be obtained on the basis of a configuration including respective intended process paths, which may be considered as tool entities and which may be in an up or down state, wherein the corresponding state may be identified on the basis of the status of the individual entities when referring to the truth tables as established above. In the above-described measurement technique for evaluating the state of a cluster tool, some issues may arise when applying the above-specified technique to the production environment including a variety of complex cluster tools, since the measurement results received by the above-specified technique may result in a reduced accuracy and thus confidence for the assessment of respective cluster tool states. For example, in the above-specified technique for assessing the tool state of a cluster tool in its entirety, the reconfiguration of a relatively purely performing cluster tool by adding high reliable entities, such as pass through chambers and the like, would significantly increase the MTBF value, thereby indicating an increased reliability, which may, however, be unrealistic. Furthermore, the corresponding metrics received by the above-specified technique may be less accurate when respective process entities, such as the entities 151A, 151B performing equivalent processes, are substantially identical so as to exhibit substantially the same performance. Furthermore, the MTBF value obtained for the cluster tool as a single entity is different from a corresponding value obtained by using the uptime of the cluster tool divided by the number of failures. Similarly, the MTTR (mean time to repair) value calculated from the mean time between failure and downtime differs from the downtime divided by the number of failures. It appears that the MTBF value and the MTTR value may be unrealistic values for the above-specified example, since 168 hours divided by the sum of 76 hours and 24.8 hours representing the mean time between failure and the mean time to repair, respectively, yields approximately 1.7 failure plus repair events per week for the cluster tool 150 in its entirety, wherein solely the entity 152C had already two failures and repairs per week, thereby causing 100% downtime to the entire cluster tool 150. As a consequence, the measurement of cluster tool characteristics, such as reliability, availability and maintainability according to conventional techniques may yield less reliable results, thereby significantly affecting production control in a semiconductor facility.
In view of the situation described above, there exists a need for an enhanced technique for assessing cluster tools, in which one or more of the problems identified above may be avoided or the effects thereof at least significantly be reduced.